Computer Science > Machine Learning

Title:Deep processing of structured data

Abstract: We construct a general unified framework for learning representation of
structured data, i.e. data which cannot be represented as the fixed-length
vectors (e.g. sets, graphs, texts or images of varying sizes). The key factor
is played by an intermediate network called SAN (Set Aggregating Network),
which maps a structured object to a fixed length vector in a high dimensional
latent space. Our main theoretical result shows that for sufficiently large
dimension of the latent space, SAN is capable of learning a unique
representation for every input example. Experiments demonstrate that replacing
pooling operation by SAN in convolutional networks leads to better results in
classifying images with different sizes. Moreover, its direct application to
text and graph data allows to obtain results close to SOTA, by simpler networks
with smaller number of parameters than competitive models.